class_docstr: ## Compressive Transformer Model
  
  This consists of multiple compressive transformer layers
code: def __init__(self, layer: CompressiveTransformerLayer, n_layers: int):
      super().__init__()
      # Make copies of the transformer layer
      self.layers = clone_module_list(layer, n_layers)
      # Final normalization layer
      self.norm = nn.LayerNorm([layer.size])
  
imports: from typing import Optional, List
  import torch
  import torch.nn.functional
  from torch import nn
  from labml_helpers.module import Module, TypedModuleList
  from labml_nn.transformers.feed_forward import FeedForward
  from labml_nn.transformers.mha import PrepareForMultiHeadAttention
  from labml_nn.transformers.xl.relative_mha import RelativeMultiHeadAttention
  from labml_nn.utils import clone_module_list
label_desc: Use image node_id 1 to create a new CompressiveTransformer object from inherited base classes: Module with example: obj = CompressiveTransformer(layer, n_layers)